\(\mathcal {L}^{ED}\) : Self-supervised Encoder-Decoder training on unpaired Natural Images & Depth Maps (Fig REF d2). This objective enables to train on any desired unpaired image along with its corresponding depth map (images for which fMRI was never recorded), well beyond the 1200 images included in the fMRI dataset. In particular, we used \(\sim \) 50K additional natural images from ImageNet's 1000-class data [1]}, along with their estimated depth-maps (see Sec. ).
We train on such RGBD data without fMRI, by imposing cycle consistency through our Encoder-Decoder transformation:
\(s\mapsto \hat{s}_{EncDec}=D\left(E\left(s\right)\right).\) 
